Social Activity Networks Shaping St. Petersburg

Landsman, David; Kats, Philipp; Nenko, Aleksandra; Kudinov, Sergei; Sobolevsky, Stanislav

doi:10.24251/hicss.2021.140

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…Also, they unravel how human behavior influences, and is influenced by, the urban environment, suggesting quantitative indicators to control integration and segregation of human flows. Also, Landsman et al [36] leverage geo-tagged data from many public datasets of St. Petersburg for building a multi-layered social activity network. They reveal meaningful socio-economic patterns across the city and provide valuable insights into the urban structure.…”

Section: Related Workmentioning

confidence: 99%

Assessing Large-Scale Power Relations among Locations from Mobility Data

Oliveira

Melo

Viana

2021

ACM Trans. Knowl. Discov. Data

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The pervasiveness of smartphones has shaped our lives, social norms, and the structure that dictates human behavior. They now directly influence how individuals demand resources or interact with network services. From this scenario, identifying key locations in cities is fundamental for the investigation of human mobility and also for the understanding of social problems. In this context, we propose the first graph-based methodology in the literature to quantify the power of Point-of-Interests (POIs) over its vicinity by means of user mobility trajectories. Different from literature, we consider the flow of people in our analysis, instead of the number of neighbor POIs or their structural locations in the city. Thus, we modeled POI’s visits using the multiflow graph model where each POI is a node and the transitions of users among POIs are a weighted direct edge. Using this multiflow graph model, we compute the attract, support, and independence powers . The attract power and support power measure how many visits a POI gathers from and disseminate over its neighborhood, respectively. Moreover, the independence power captures the capacity of a POI to receive visitors independently from other POIs. We tested our methodology on well-known university campus mobility datasets and validated on Location-Based Social Networks (LBSNs) datasets from various cities around the world. Our findings show that in university campus: (i) buildings have low support power and attract power ; (ii) people tend to move over a few buildings and spend most of their time in the same building; and (iii) there is a slight dependence among buildings, even those with high independence power receive user visits from other buildings on campus. Globally, we reveal that (i) our metrics capture places that impact the number of visits in their neighborhood; (ii) cities in the same continent have similar independence patterns; and (iii) places with a high number of visitation and city central areas are the regions with the highest degree of independence.

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Section: Related Workmentioning

confidence: 99%

Assessing Large-Scale Power Relations among Locations from Mobility Data

Oliveira

Melo

Viana

2021

ACM Trans. Knowl. Discov. Data

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“…Thus understanding the underlying community structure of the networks saw a wide range of applications, including social science (Plantié and Crampes 2013), biology (Guimerà and Nunes Amaral 2005), and economics (Piccardi and Tajoli 2012). In particular, partitioning the networks of human mobility and interactions is broadly applied to regional delineation (Ratti et al 2010;Blondel et al 2010;Sobolevsky et al 2013;Amini et al 2014;Hawelka et al 2014;Kang et al 2013;Belyi et al 2017;Grauwin et al 2017;Xu et al 2021) as well as urban zoning (Sobolevsky et al 2018;Landsman et al 2020Landsman et al , 2021.…”

Section: Introductionmentioning

confidence: 99%

Graph neural network inspired algorithm for unsupervised network community detection

Sobolevsky

Belyi

2022

Appl Netw Sci

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Network community detection often relies on optimizing partition quality functions, like modularity. This optimization appears to be a complex problem traditionally relying on discrete heuristics. And although the problem could be reformulated as continuous optimization, direct application of the standard optimization methods has limited efficiency in overcoming the numerous local extrema. However, the rise of deep learning and its applications to graphs offers new opportunities. And while graph neural networks have been used for supervised and unsupervised learning on networks, their application to modularity optimization has not been explored yet. This paper proposes a new variant of the recurrent graph neural network algorithm for unsupervised network community detection through modularity optimization. The new algorithm’s performance is compared against the state-of-the-art methods. The approach also serves as a proof-of-concept for the broader application of recurrent graph neural networks to unsupervised network optimization.

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Deep Learning Attention Model for Supervised and Unsupervised Network Community Detection

Sobolevsky

2023

Lecture Notes in Computer Science

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Social Activity Networks Shaping St. Petersburg

Cited by 3 publications

References 35 publications

Assessing Large-Scale Power Relations among Locations from Mobility Data

Assessing Large-Scale Power Relations among Locations from Mobility Data

Graph neural network inspired algorithm for unsupervised network community detection

Deep Learning Attention Model for Supervised and Unsupervised Network Community Detection

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